{"ID":2876782,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00132","arxiv_id":"2509.00132","title":"CoComposer: LLM Multi-agent Collaborative Music Composition","abstract":"Existing AI Music composition tools are limited in generation duration, musical quality, and controllability. We introduce CoComposer, a multi-agent system that consists of five collaborating agents, each with a task based on the traditional music composition workflow. Using the AudioBox-Aesthetics system, we experimentally evaluate CoComposer on four compositional criteria. We test with three LLMs (GPT-4o, DeepSeek-V3-0324, Gemini-2.5-Flash), and find (1) that CoComposer outperforms existing multi-agent LLM-based systems in music quality, and (2) compared to a single-agent system, in production complexity. Compared to non- LLM MusicLM, CoComposer has better interpretability and editability, although MusicLM still produces better music.","short_abstract":"Existing AI Music composition tools are limited in generation duration, musical quality, and controllability. We introduce CoComposer, a multi-agent system that consists of five collaborating agents, each with a task based on the traditional music composition workflow. Using the AudioBox-Aesthetics system, we experimen...","url_abs":"https://arxiv.org/abs/2509.00132","url_pdf":"https://arxiv.org/pdf/2509.00132v1","authors":"[\"Peiwen Xing\",\"Aske Plaat\",\"Niki van Stein\"]","published":"2025-08-29T14:15:12Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"cs.MM\",\"eess.AS\"]","methods":"[\"Large Language Model\"]","has_code":false}