{"ID":5937651,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T09:59:57.507513563Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04212","arxiv_id":"2607.04212","title":"An Evaluation of Role-Based Multi-Agent Code Generation on Repository-Scale Problems","abstract":"Role-based multiagent code generation aims to make LLMs more effective on repository-scale problems, moving beyond small programming tasks. We evaluate this approach on 12 Java repositories, finding greater similarity to developer code than single LLMs, but a persistent gap from human implementations.","short_abstract":"Role-based multiagent code generation aims to make LLMs more effective on repository-scale problems, moving beyond small programming tasks. We evaluate this approach on 12 Java repositories, finding greater similarity to developer code than single LLMs, but a persistent gap from human implementations.","url_abs":"https://arxiv.org/abs/2607.04212","url_pdf":"https://arxiv.org/pdf/2607.04212v1","authors":"[\"Benedetta Donato\",\"Noah Hagar-Dent\",\"Aaron Worsnop\",\"Leonardo Mariani\",\"Valerio Terragni\"]","published":"2026-07-05T10:04:15Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}