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arXiv · 2609.32264

LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models

Pavel Tikhonov·Elena Tutubalina·Ivan Oseledets·Dmitry I. Ignatov·Mikhail Seleznyov
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Abstract

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.

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Pavel Tikhonov, Elena Tutubalina, Ivan Oseledets, Dmitry I. Ignatov, Mikhail Seleznyov. 2026-09-26. LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models. https://arxiv.org/abs/2609.32264

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