Learning to Discover Interesting Mathematics
A 27B model ranks mathematical theorems by interestingness and cuts Mathlib overlap from 91.9% to 30.6%.
The paper defines a theorem's intrinsic interestingness as the ratio of its proof length to its statement length and reports a strong correlation with downstream utility. The authors train a 27B model that predicts proof difficulty more accurately than frontier general-purpose models. Optimizing for the metric yields more interesting theorems and reduces substantial or full overlap with Mathlib from 91.9% to 30.6%. The system can generate candidates, select the most interesting, and iteratively grow a machine-verified library without human-supplied targets.
- Interestingness is proof length divided by statement length
- A 27B model predicts proof difficulty better than frontier LLMs
- Substantial Mathlib overlap fell from 91.9% to 30.6%
- The system can iteratively expand a verified math library
From the original article
1 min read at huggingface.co
Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for decades. This opens the door to expansion of mathematical knowledge at unprecedented scale. Yet, while LLMs may be able to conjecture and prove more and more theorems, it remains open whether this new mathematical knowledge is interesting or useful.
By Niket Patel, Ahmad Rammal, Amaury Hayat · huggingface.co
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