Libretto：賦予LLM智能體音樂結構感

摘要

生成式音乐系统目前能够根据文本提示生成令人印象深刻的音频，但音频输出在音乐结构上的检视、编辑和诊断仍存在困难。我们提出Libretto，一个面向智能体的符号音乐生成与修订框架。该框架采用基于大语言模型的原生语法，明确划分了起始时间片、声部及小节层级组织，随后在经语料库校准的统计空间中，从节奏、和声、旋律、织体、结构形式及变奏维度对每首作品进行评估。同样的结构轴支持检索、诊断、抄袭风险控制及迭代式自我修正。在空缺填充、参考引导的全曲生成、渐进式变形以及教育型音乐生成等任务中，Libretto将符号音乐从原始词元序列转化为可供语言模型智能体测量与编辑的对象。

English

Generative music systems can now produce impressive audio from text prompts, but audio outputs are difficult to inspect, edit, and diagnose as musical structure. We introduce Libretto, an agent-facing framework for symbolic music generation and revision. Libretto uses an LLM-native grammar with explicit onset slots, voices, and bar-level organization, then evaluates each piece in a corpus-calibrated statistical space over rhythm, harmony, melody, texture, form, and variation. The same structural axes support retrieval, diagnosis, copy-risk control, and iterative self-revision. Across gap filling, reference-guided full-piece generation, gradual morphing, and educational music generation, Libretto turns symbolic music from a raw token sequence into a measurable and editable object for language-model agents.