Pith Number

pith:ZTRQ4XAP

pith:2026:ZTRQ4XAPDCZ5N5Q6CWBHGFWY3Q

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APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music

Dorien Herremans, Jaavid Aktar Husain

A multi-task model trained on AI music predicts both popularity and aesthetic quality, and the aesthetic signals improve human preference predictions on entirely unseen generators.

arxiv:2605.03395 v2 · 2026-05-05 · cs.SD · cs.AI · cs.LG · cs.MM

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Claims

C1strongest claim

in an out-of-distribution evaluation on the Music Arena dataset, comprising pairwise human preference battles across eleven generative music systems unseen during training, including aesthetic features consistently improves preference prediction, demonstrating strong generalisation of the learned representations across generative architectures.

C2weakest assumption

That the five perceptual aesthetic quality dimensions extracted from frozen MERT embeddings capture complementary information to engagement signals and generalize to unseen generative architectures without overfitting to the Suno/Udio training distribution.

C3one line summary

APEX jointly predicts engagement-based popularity and five aesthetic quality dimensions for AI-generated music, improving human preference prediction on out-of-distribution generative systems.

Cited by

1 paper in Pith

APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music

Receipt and verification

First computed	2026-06-09T01:04:43.361955Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

cce30e5c0f18b3d6f61e15827316d8dc31b4659c257c206914f1659d257a08c9

Aliases

arxiv: 2605.03395 · arxiv_version: 2605.03395v2 · doi: 10.48550/arxiv.2605.03395 · pith_short_12: ZTRQ4XAPDCZ5 · pith_short_16: ZTRQ4XAPDCZ5N5Q6 · pith_short_8: ZTRQ4XAP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZTRQ4XAPDCZ5N5Q6CWBHGFWY3Q \
  | 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: cce30e5c0f18b3d6f61e15827316d8dc31b4659c257c206914f1659d257a08c9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f99b56abb85bed53837296454d5e2d7d8d882612c0e8b55355df9debe850eb4f",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG",
      "cs.MM"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.SD",
    "submitted_at": "2026-05-05T06:11:51Z",
    "title_canon_sha256": "0ed72017a3cb0a8029e478b9a8232d4f6e350c0c91aa4b77c1ad580956760f69"
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  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}