Nothing from Something: Can a Language Model Discover 0?
AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge. A key question for these systems is how much they can reach beyond their training data. Mathematical discovery requires a strong form of out of distribution generalization; the ability to hypothesize genuinely new - and potentially logically more powerful - mathematical structures. It has been hypothesized that language abilities support such generalizations in human cognition. In this work, we use simple arithmetic as a case study for examining how modern AI models could expand their mathematical horizons, evaluating whether these models can indepen...
| Authors | Phoebe Zeng, Thomas L. Griffiths, Brenden M. Lake |
|---|---|
| Primary category | cs.AI |
| Published | 15 June 2026 |
| Last updated at source | 18 June 2026 |
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