LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models

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.

Computation and Language (cs.CL)

arXiv:2609.32264 [cs.CL]

(or arXiv:2609.32264v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.32264 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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