TREAT: Evaluating Access to Formal Knowledge across Equivalent Mathematical Representations
AI systems increasingly operate between flexible input representations and formal objects used by downstream tools. A key challenge is recognizing when an unfamiliar formulation denotes a known formal object. We study this challenge through theorem recognition: given an equivalence-preserving transformation of a theorem condition, a model must recover the theorem identity associated with the standard statement. We introduce TREAT, a benchmark for evaluating whether large language models can recover known theorem identities from equivalence-preserving formula-level transformations. Rather than paraphrasing theorem text, TREAT changes the mathematical form of theorem conditions themselves, ...
| Authors | Fateme Mazdarani, Carlos Toxtli |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 10 September 2026 |
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