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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint introduces Generative Verification (GenV), a machine-learning-based approach to detect failures in formal theorem-proving translations where incorrect encodings pass syntactic checks. The method achieves 0.961 AUROC on held-out verification tasks and shows 11.3-point accuracy gain on downstream allocation, but evaluation is limited to oracle-mined data without peer review or independent validation.
Algorithm development with offline oracle-mined evaluation and mechanistic analysis. Formal verification tasks in neurosymbolic systems using Z3 solver and language models; scope limited to autoformalization domain.. Intervention: Generative Verification (GenV): a language-model-based continuous reference-equivalence scorer trained on Z3-oracle-mined equivalence labels, distilled without explicit reference formalization.
GenV+HN achieves 0.961 AUROC in reference-equivalence verification 11.3-point downstream accuracy gain in agentic test-time compute allocation Zero-shot generalization across unseen translators and divergent formal styles reported
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A novel method for detecting failures in formal verification systems, demonstrated on a curated dataset with strong offline metrics but limited real-world deployment evidence and no peer review.
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Neurosymbolic systems rely on mathematical solvers to guarantee reasoning correctness, yet solvers are fundamentally blind to whether a formal translation maintains strict reference-equivalence to a designated formalization. We formalize this vulnerability as Verdict-Preserving-Unfaithfulness (VPU): a failure mode where an incorrect encoding executes successfully and matches the expected verdict. We theoretically prove that structural, verdict-only verification heuristics are mathematically bounded to chance-level detection on these deceptively valid traces. To resolve this, we introduce Generative Verification (GenV), which distills an offline Z3-equivalence oracle into a reference-free, continuous reference-equivalence score by repurposing the language model's native vocabulary space. Mechanistic analysis via decision-projected logit lenses and sparse autoencoders shows this generative readout natively extracts precise spatial error coordinates without explicit localization training. Empirically, our oracle-mined verifier (GenV+HN) achieves 0.961 AUROC in reference-equivalence verification, generalizes zero-shot across unseen translators and divergent formal styles, and yields an 11.3-point downstream accuracy gain in agentic test-time compute allocation.
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