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Formal verification provides the strongest possible guarantees, but the ability of AI models to work with verification-aware languages is hindered by the scarcity of human-written examples of programs in those languages. To tackle this prevalent data scarcity issue, we propose Formal Disco: a distributed system for coordination of LLM-based workers that can be easily applied to open-ended synthetic data generation at scale. We use Formal Disco to share tasks and programs between three classes of workers: \"initiators\", which read random READMEs from open-source repositories and documentation snippets to sketch a related verified program, \"fixers\" which take compiler and verifier feedback and attempt to resolve issues, and \"extenders\" that take working programs and propose patches to expand","title":"Formal Disco: Scalable Open-Ended Generation of Formally Verified Programs","url":"https://arxiv.org/abs/2607.04631","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.04631v1 Announce Type: new \nAbstract: The cost of producing code is rapidly diminishing with increasingly capable AI agents, while quality assurance of generated programs has not kept pace. Formal verification provides the strongest possible guarantees, but the ability of AI models to work with verification-aware languages is hindered by the scarcity of human-written examples of programs in those languages. To tackle this prevalent data scarcity issue, we propose Formal Disco: a distributed system for coordination of LLM-based workers that can be easily applied to open-ended synthetic data generation at scale. 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