Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation
Score
77首次将音乐表示从模型规模等变量中隔离出来并给出反直觉实证，信息增量高。
对照实验严谨、统计置信区间明确，附预注册人类研究与丰富开源资源，质量上乘。
为从业者提供可直接使用的评测框架、检查点与语料，且解码期约束可低成本落地。
聚焦当前LLM原生音乐生成的分词瓶颈，发布大规模新语料，方法新鲜且正处热点。
来自arXiv预印本，尚未同行评审，但实验设计详尽且开源资源可验证。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper's controlled design is rigorous, its finding is counterintuitive yet reproducible, and the extensive open-source release makes it highly credible and methodologically valuable.
AI commentary
「The real contribution is not another SOTA score, but turning 'does tokenization matter?' into a measurable fact. Teams working on music AIGC should try a representation change before scaling up further, and evaluation-focused readers will find the released corpora and diagnostic immediately useful.」
Quick overview
This paper isolates tokenization from all other variables by fixing pretrained Qwen3.5 backbones (0.8B–27B), data, budget, and decoding, then swapping only the music representation across seven tokenizations. The results are striking: representation, not model size, is the binding variable for distributional fidelity. Scaling the backbone 34x barely moves Fréchet Music Distance (FMD), while changing representation halves it. PMT, a performance-resolution stream with 10 ms timing, per-note velocity, multi-track texture, and 609 symbols, reaches FMD 159 at 0.8B, beating beat-grid tokenizations at 27B. The effect persists on a from-scratch 26M model and a second tokenizer, indicating it is a property of the class. Caption adherence is weak but separable; a lightweight decode-time constraint sharply improves instrument and key accuracy. The authors release a harness, 25+ checkpoints, two corpora (86.6k aligned; 6.25M captioned), and a diagnostic showing published systems largely ignore captions.
- With all else fixed, swapping only music representation matters more than model size: 34x scaling barely changes FMD, while representation change halves it.
- PMT (10ms timing, per-note velocity, multi-track, 609 symbols) achieves FMD 159 at 0.8B, beating a 27B beat-grid model and replicating on a 26M from-scratch backbone.
- Native caption adherence is weak but separable: a light decode-time constraint lifts instrument F1 from .28 to .60 and Correct-Key from .16 to .35 with no distributional cost.
- They release the eval harness, 25+ checkpoints, an 86.6k aligned corpus, and a 6.25M captioned corpus, plus an imprinting diagnostic for future representation claims.
Full text
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