{
  "id": 3255,
  "url": "https://arxiv.org/abs/2607.01248v1",
  "title": "A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content",
  "summary": "Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these settings, users may obtain highly structured answers, plans, and judgments without sufficient domain practice. This paper proposes a practice auditing framework for LLM use and AI-generated content governance. It introduces collective empiricism to describe how LLMs compress and reorganize large-scale human experience into outputs that appear empirical an",
  "authors": "Yang Zhao, Yingshuo Li, Zeyu Zhang",
  "category": "research",
  "topics": "regulation,jobs-economy,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-02T00:46:47.000Z",
  "fetched_at": "2026-07-14T16:30:05.532Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3255",
  "original_url": "https://arxiv.org/abs/2607.01248v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}