{
  "id": 942,
  "url": "https://arxiv.org/abs/2606.17006v1",
  "title": "TuneJury: An Open Metric for Improving Music Generation Preference Alignment",
  "summary": "We introduce TuneJury, an open, instance-level pairwise reward model for text-to-music that predicts a music preference score from a text prompt and an audio clip. The released checkpoint is trained on publicly available human-preference labels covering arena-style (A vs. B) votes, metric-alignment preference pairs, crowdsourced pairwise comparisons, and expert aesthetic ratings. The predicted score margin between two clips is well calibrated on our held-out test split, supporting data filtering",
  "authors": "Yonghyun Kim, Junwon Lee, Haiwen Xia, Yinghao Ma, Junghyun Koo, Koichi Saito et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-15T17:39:30.000Z",
  "fetched_at": "2026-07-14T14:14:54.533Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/942",
  "original_url": "https://arxiv.org/abs/2606.17006v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}