Pith Number

pith:ELX6Y2U2

pith:2026:ELX6Y2U2BD333F24H6BC3VABOS

not attested not anchored not stored refs resolved

S2Accompanist: A Semantic-Aware and Structure-Guided Diffusion Model for Music Accompaniment Generation

Chunbo Hao, Guobin Ma, Hanbing Zhang, Huakang Chen, Lei Xie, Mengqi Wei, Pengcheng Zhu, Wenkai Cheng, Yuxuan Xia, Zhixian Zhao

A 402-million-parameter diffusion model generates coherent music accompaniments with localized semantic control by creating segment-level metadata and embedding musical structures in its latent space.

arxiv:2605.17414 v1 · 2026-05-17 · eess.AS

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{ELX6Y2U2BD333F24H6BC3VABOS}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

S2Accompanist achieves state-of-the-art objective performance on the ATTM Grand Challenge benchmark across both the Efficiency and Performance Tracks with only 402M parameters.

C2weakest assumption

The automated data pipeline (structural segmentation, Large Audio-Language Model driven segment-level captioning, and dual-metric quality grading) successfully creates high-quality localized metadata that existing coarse track-level annotations lack.

C3one line summary

S2Accompanist is a 402M-parameter semantic-aware diffusion model that achieves SOTA on the ATTM Grand Challenge benchmark for music accompaniment generation via automated data processing and structure-guided VAE fine-tuning.

References

26 extracted · 26 resolved · 4 Pith anchors

[1] Diffrhythm+: Controllable and flexible full-length song generation with preference optimization, 2025

[2] Diffrhythm: Blazingly fast and embarrassingly sim- ple end-to-end full-length song generation with latent diffusion 2025

[3] Ace-step: A step towards music generation foundation model.arXiv preprint arXiv:2506.00045 2025

[4] arXiv preprint arXiv:2602.00744(2026) 2026

[5] Noise2Music: Text-conditioned music generation with diffusion models.arXiv preprint arXiv:2302.03917 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:57.271216Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

22efec6a9a08f7bd975c3f822dd40174b3dffb5740c4b4acdc6adb0c3f880101

Aliases

arxiv: 2605.17414 · arxiv_version: 2605.17414v1 · doi: 10.48550/arxiv.2605.17414 · pith_short_12: ELX6Y2U2BD33 · pith_short_16: ELX6Y2U2BD333F24 · pith_short_8: ELX6Y2U2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ELX6Y2U2BD333F24H6BC3VABOS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 22efec6a9a08f7bd975c3f822dd40174b3dffb5740c4b4acdc6adb0c3f880101

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6c266279a7f23004bd41c2981b37c4793b53077809abdc7fb2882a318958e829",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.AS",
    "submitted_at": "2026-05-17T12:22:17Z",
    "title_canon_sha256": "df99a9853bcc8fb5a0e59412eb37ebe340d10c96648922c8f3f353d2c48cae11"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17414",
    "kind": "arxiv",
    "version": 1
  }
}