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ARAşTıRMA · YAPAY ZEKA arXiv:2610.20573 14 Eki 2026 · v1

Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization

Anna Mei, Michael R. Douglas

YAYIN:14 Eki 2026 ALAN:cs.LO OKUMA:8

Özet

We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting errors missed by LLM judges. We apply this method, along with our Extract-Verify-Proof autoformalization pipeline, to construct the Formalized Library of Mathematical Functions (FLMF), a partial formalization of the NIST Digital Library of Mathematical Functions (DLMF). On three labeled datasets, our method achieves the lowest accepted-error rate among the evaluated methods, measured as the fraction of incorrect statements accepted by the verification method. Expert review of the results also identifies translation and source defects in existing datasets. Code available at https://github.com/annamei1/FLMF-Ch7ErrFunc-Release.git.

Özetle: We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness.

Özet

We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting errors missed by LLM judges. We apply this method, along with our Extract-Verify-Proof autoformalization pipeline, to construct the Formalized Library of Mathematical Functions (FLMF), a partial formalization of the NIST Digital Library of Mathematical Functions (DLMF). On three labeled datasets, our method achieves the lowest accepted-error rate among the evaluated methods, measured as the fraction of incorrect statements accepted by the verification method. Expert review of the results also identifies translation and source defects in existing datasets. Code available at https://github.com/annamei1/FLMF-Ch7ErrFunc-Release.git.

Orijinal Özet (İngilizce)

We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting errors missed by LLM judges. We apply this method, along with our Extract-Verify-Proof autoformalization pipeline, to construct the Formalized Library of Mathematical Functions (FLMF), a partial formalization of the NIST Digital Library of Mathematical Functions (DLMF). On three labeled datasets, our method achieves the lowest accepted-error rate among the evaluated methods, measured as the fraction of incorrect statements accepted by the verification method. Expert review of the results also identifies translation and source defects in existing datasets. Code available at https://github.com/annamei1/FLMF-Ch7ErrFunc-Release.git.

Kaynak: arXiv:2610.20573 · PDF

BibTeX

@article{mei2026beyond,
  title   = {Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization},
  author  = {Anna Mei and Michael R. Douglas},
  journal = {arXiv preprint arXiv:2610.20573},
  year    = {2026},
  url     = {https://arxiv.org/abs/2610.20573}
}

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