Operationalizing theorem interestingness for open-ended exploration
This paper studies what makes mathematical theorems interesting by modeling how mathematicians select results in formal libraries and papers. The authors compare feature-based evaluators against a large language-model judge, finding that ex-ante features predict selections as well as the LLM and that the two complement each other. Interestingness is largely shared across mathematicians, with structural features like statement and proof length carrying most of the signal, while subjective variation depends on practices and individual authors. The approach can be applied to other sciences.
Provides a concrete, recent method for quantifying interestingness in AI-driven exploration, with empirical comparison to LLM judges, useful for automated conjecture generation.
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