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Yet standard single-output evaluation protocols collapse a many-to-many problem into a single-output prediction task. For downstream proving, this granularity is too coarse: a formal statement is not merely a faithful translation endpoint, but also a prover-facing interface whose structure can alter proof search under a fixed budget. We therefore recast autoformalization as budgeted test-time search: FormalEvolve maintains a compilation-feasible archive for reuse, while reporting the deduplicated semantically accepted repertoire for evaluation and downstream proving. It expands the archive with LLM-driven mutation, crossover, bounded patch repair, and symbolic Abstract Syntax Tree (AST) rewrites for structural diversity. Under a generator-call budget of T=100 with a fixed LLM semantic judge, FormalEvo","title":"FormalEvolve: Neuro-Symbolic Evolutionary Search for Diverse Autoformalization","url":"https://arxiv.org/abs/2603.19828","vendor":"arxiv_cs_ai"},"summary":"arXiv:2603.19828v3 Announce Type: replace \nAbstract: Autoformalization aims to produce formal statements that compile and faithfully preserve the intended meaning of informal mathematics. Yet standard single-output evaluation protocols collapse a many-to-many problem into a single-output prediction task. For downstream proving, this granularity is too coarse: a formal statement is not merely a faithful translation endpoint, but also a prover-facing interface whose structure can alter proof search under a fixed budget. We therefore recast autoformalization as budgeted test-time search: FormalEvolve maintains a compilation-feasible archive for reuse, while reporting the deduplicated semantically accepted repertoire for evaluation and downstream proving. It expands the archive with LLM-driven mutation, crossover, bounded patch repair, and symbolic Abstract Syntax Tree (AST) rewrites for structural diversity. 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