Learning to Discover Interesting Mathematics
Quick summary
arXiv:2609.28603v1 Announce Type: cross Abstract: 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. We define intrinsic interestingness of a theorem as the ratio between the length of its proof and the length of its statement. We show that this correlat
Key takeaways
- arXiv:2609.28603v1 Announce Type: cross Abstract: 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.
Why it matters
“Learning to Discover Interesting Mathematics” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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