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MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving vi...
[Submitted on 11 Dec 2025 (v1), last revised 24 Jun 2026 (this v · 2026-06-25 · via cs.LG updates on arXiv.org

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Abstract:LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs. Interactive theorem provers (ITPs) dominate mathematical reasoning but require detailed low-level proof steps, while auto-active verifiers offer automation but focus on software verification. Recent work has begun bridging this divide by evaluating LLMs for software verification in ITPs, but the complementary direction, LLMs for mathematical theorem proving in auto-active verifiers, remains unexplored. We present MINIF2F-DAFNY, the first translation of the widely-used mathematical benchmark miniF2F to an auto-active verifier: Dafny. We find that Dafny's automation alone solves 39-44% of problems with empty proofs, whereas many require substantial proof guidance in ITPs. We evaluate 8 off-the-shelf LLMs on proof generation, with the best model (Claude Opus 4.6) achieving 62.7% cumulative pass@4 on the full test set, improving over the 38.9% empty-proof baseline by 23.8 percentage points. These results show that auto-active verification offers a complementary empirical setting for AI-assisted mathematical reasoning, where LLMs provide high-level guidance while SMT automation handles low-level details. Our benchmark and evaluation infrastructure are publicly available on this https URL.

Submission history

From: Mantas Baksys [view email]
[v1] Thu, 11 Dec 2025 00:52:19 UTC (96 KB)
[v2] Mon, 2 Feb 2026 16:50:54 UTC (61 KB)
[v3] Wed, 24 Jun 2026 11:59:00 UTC (70 KB)