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LANTERN is a fast pipeline that uses a classifier over pretrained language model activations to rank candidate mathematical relations, then filters, generates, and verifies hypotheses. Applied to OEIS, it found 62 verified relations, 9 informative ones, and 4 entirely novel mathematical connections, completing the whole process in under 8 hours.
This paper proposes a self-supervised theorem-discovery algorithm that starts from axioms and inference rules alone, building a theorem library without human priors. Experiments show the discovered theorems are meaningful and improve LLM proof performance.