Large language models can propose proofs for interactive theorem provers, but a successful build does not show the surrounding verification task was preserved. We study this problem in an Isabelle development of a sampled-data double-tank controller. The work began with nine theories and ten unfinished obligations, grew to a 16-theory build without sorry, oops, added axiomatisation, or oracle use, and accumulated 23 stable and 36 broken proof states. A retrospective audit found material changes in 16 of the 100 original declarations, including a weakened end-to-end assurance theorem that assumed three of the four requirements in its conclusion. We used CAPRI, a contract-aware proof-repair tool, to govern a reconstruction by combining Isabelle acceptance with an independent check of repository changes against machine-readable edit contracts. The reconstruction discharged all ten scoped obligations within the original nine-theory structure. A secondary replay by a co-author reproduced the R10 build, contract checks, control tests, and principal audit findings; independent replication remains future work. Operational end-to-end verification remains incomplete: we still need to connect operational executions to the reconstructed quantitative trace contract, a task requiring an extended contract.
Jim Woodcock, Gabriel Leite, Augusto Sampaio et al.· 0 citations
CAPRI is presented, a contract-aware repair workflow in which Isabelle checks the proof and an independent checker enforces a machine-readable edit contract, in which Isabelle checks the proof and an independent checker enforces a machine-readable edit contract.
Jim Woodcock, Gabriel Leite, Augusto Sampaio et al.· 1 citation
RADIANT is an engineering methodology that combines MDE with Multi-Agent Large Language Models (LLMs) for complete model-based system development, with a focus on safety-critical systems and finds that the multi-agent decomposition reliably improves theSyntactic validity of the generated formal artefacts over a single-agent baseline, while gains in semantic accuracy are model-dependent.
Ran Wei, Letian Zhu, Haochi Wang et al.· arXiv.org· 0 citations
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