{
  "id": 975,
  "url": "https://arxiv.org/abs/2606.16276v2",
  "title": "SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data",
  "summary": "As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but instead by provider- or application-specific model specifications. These specifications are typically long, structured, and frequently updated, yet existing alignment pipelines lack a systematic mechanism to operationalize them as training signals. In this paper, we propose specification-grounded alignment, a new alignmen",
  "authors": "Wenjie Wang, Yue Huang, Zhengqing Yuan, Han Bao, Shiyi Du, Yuchen Ma et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-15T06:30:52.000Z",
  "fetched_at": "2026-07-14T14:14:54.535Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/975",
  "original_url": "https://arxiv.org/abs/2606.16276v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}