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pith:2026:MKAB34CSDUFR22BEBBUUD2AILN
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Shao: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation

Feng Yu, Hongjia Liu, Huijing Liang, Jiafeng Liu, Maosong Sun, Wenbo Zhan, Xiaobing Li, Yuanliang Dong, Yuming Sun, Yuqing Cheng, Zhancheng Guo

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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.

Receipt and verification
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

62801df0521d0b1d6824086941e8085b6cfcd12566c2fb646448f2db2fdc6638

Aliases

arxiv: 2605.01790 · arxiv_version: 2605.01790v2 · doi: 10.48550/arxiv.2605.01790 · pith_short_12: MKAB34CSDUFR · pith_short_16: MKAB34CSDUFR22BE · pith_short_8: MKAB34CS
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MKAB34CSDUFR22BEBBUUD2AILN \
  | jq -c '.canonical_record' \
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Canonical record JSON
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    "submitted_at": "2026-05-03T09:13:20Z",
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