pith:MKAB34CS
Shao: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation
High-fidelity music can be generated by progressively modeling structure and detail together inside one 64-layer acoustic token hierarchy.
arxiv:2605.01790 v2 · 2026-05-03 · cs.SD · cs.AI
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Claims
Taken together, our results suggest that high-quality music generation can be effectively pursued without separating structure and fidelity into heterogeneous representation spaces. Instead, both can be progressively modeled within a unified acoustic-token hierarchy, pointing toward a simpler and more unified path to high-quality music generation.
That a single 64-layer RVQ acoustic token space can capture both high-level musical structure and fine-grained fidelity sufficiently to allow emergent alignment and high quality without information loss that would require separate semantic modeling.
A two-stage coarse-to-fine acoustic token language model with hybrid attention generates high-fidelity music and achieves emergent lyric alignment without separate semantic tokens.
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| First computed | 2026-07-07T02:19:51.792108Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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