ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction
Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules. Whether such an output that appears creative is truly original and effective is hard to establish: open-ended outputs require subjective judgment, the output may replicate something seen in training, or the task may be too simple to need creativity. We present ALPS (Austin-Law Proof-Synthesis), a benchmark that designs a task to measure valid creativity: producing a solution that is original and can be proven correct. Each instance is a single equational law, certified to require either the construction of an infinite mathematical structure satisfying the law, or a proof that no such st...
| Authors | Eric Xie, Wenqian Ye, Aidong Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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