{"ID":5935838,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03130","arxiv_id":"2607.03130","title":"Copper: Unifying Correctness and Performance Specification in Code Generation","abstract":"Generative AI has made remarkable progress in producing functionally correct code, yet ensuring both correctness and performance remains an open challenge. We present Copper, a framework that combines formal verification with performance-aware specification to generate code that is provably correct and efficiently executable. Our approach integrates AI-driven code synthesis with formal verification tools, and automated performance profiling loops. Evaluated on a diverse set of algorithmic and real-world programming tasks, Copper produces solutions that satisfy strict correctness guarantees while delivering significant improvements in runtime and memory efficiency compared to baseline AI-generated code. This work demonstrates that it is feasible to bridge the gap between trustworthiness and performance in AI-assisted programming, offering a practical pathway toward reliable, high-performance code generation.","short_abstract":"Generative AI has made remarkable progress in producing functionally correct code, yet ensuring both correctness and performance remains an open challenge. We present Copper, a framework that combines formal verification with performance-aware specification to generate code that is provably correct and efficiently exec...","url_abs":"https://arxiv.org/abs/2607.03130","url_pdf":"https://arxiv.org/pdf/2607.03130v1","authors":"[\"André Lizardo\",\"Raul Barbosa\"]","published":"2026-07-03T09:19:34Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[]","has_code":false}