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Author

Ferhat Erata

Yale University

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Preprint Sep 2026

SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?

This work presents Benchproofer, a pipeline that turns a coding task with a known correct patch into a formally verified one, which writes a specification for the new code, summarizes the existing functions that code calls with axioms, and admits an instance only after mechanical and adversarial gates agree.

George Ma, Benjamin Mikek, Hao-Yu Li et al. · 1 citation
#artificial intelligence Review May 2026

Fidelity Probes for Specification-Code Alignment

Fidelity probes are introduced to check a specification against the code and guide its revision, using an observability rule to focus probe questions on behaviour that the modernized system is intended to preserve, including user-visible outputs, changes to stored business data, and interactions with other systems.

Ferhat Erata, Hao Zhou, Jun Huan · 1 citation
#artificial intelligence Preprint Sep 2026

SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?

Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check correctness with held-out test suites, which are inherently incomplete and increasingly susceptible to memorization. Formal verification avoids both problems, but existing wor...

George Ma, Benjamin Mikek, Hao-Yu Li et al. · 0 citations
#machine learning Preprint Sep 2026

Beyond Solver Verdicts: Generative Reward Models for Autoformalization

Generative Verification (GenV) is introduced, which distills an offline Z3-equivalence oracle into a reference-free, continuous reference-equivalence score by repurposing the language model's native vocabulary space and theoretically proves that structural, verdict-only verification heuristics are mathematically bounde...

Vikash Singh, Debargha Ganguly, Aman Goel et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Stratified Consistency Distillation for Natural Language Formalization

A fine-tuning-based Stratified Consistency Distillation approach that shows significant and consistent improvements in both Pass@K and the novel Equivalent Logical Similarity metrics, demonstrating the potential of advancing logical translation through consistency distillation.

Zhi-Chao Hou, Ferhat Erata, Joseph Lilien et al. · 1 citation

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