{
  "id": 18664,
  "url": "https://arxiv.org/abs/2608.10858v1",
  "title": "Auditable AI-Assisted Research Writing: An Engineering Discipline with Pre-Registered Process Observation",
  "summary": "Language models now draft, classify and criticise inside research production, yet the artifacts they help produce carry little accountable history. Rather than detecting machine involvement afterwards, we specify an auditability discipline built at production time: git sealing with an anchor lineage, hash-bound provenance, red-line gates that refuse non-compliant artifacts and log every refusal, cross-model role separation, and programmatic assembly from registered sources. Adherence is instrume",
  "authors": "Yang Zhou, Chengqun Yu",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T12:30:42.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18664",
  "original_url": "https://arxiv.org/abs/2608.10858v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}