← Saturday, September 26, 2026ResearchPaper
A proof-difficulty metric steers LLMs toward less-overlapping mathematical theorems
The work trains a 27B model to predict proof difficulty and reports reducing substantial or full overlap with Mathlib from 91.9% to 30.6%. Its metric offers a concrete approach to selecting outputs for generative research systems.
- What
- Proof difficulty conditioned on premises helps rank theorem interestingness, defined as proof length divided by statement length.
- Method
- A trained 27B model predicts proof difficulty; optimizing the metric reduced substantial or full Mathlib overlap from 91.9% to 30.6%.
- Why it matters
- Quantifiable ranking signals can guide conjecture selection and proof search in formal mathematical libraries.
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