Agentic LLM framework autoformalizes 32 Putnam problems and main theorems plus proofs from five STOC papers into Lean 4, with two proofs using only kernel axioms.
arXiv preprint arXiv:2510.06857 , year=
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The signal-coverage matrix stratifies autoformalization outputs into true success, type-only, semantic-only, and both-fail cells, showing type-correctness gains are mostly type-stratum recovery with semantic errors largely unchanged.
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
Agentic LLM framework autoformalizes 32 Putnam problems and main theorems plus proofs from five STOC papers into Lean 4, with two proofs using only kernel axioms.
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The Signal-Coverage Matrix: Stratifying Type and Semantic Errors in Statement Autoformalization
The signal-coverage matrix stratifies autoformalization outputs into true success, type-only, semantic-only, and both-fail cells, showing type-correctness gains are mostly type-stratum recovery with semantic errors largely unchanged.
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OProver: A Unified Framework for Agentic Formal Theorem Proving
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.