{
  "id": 3203,
  "url": "https://arxiv.org/abs/2606.03812v1",
  "title": "Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis",
  "summary": "Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic ",
  "authors": "Sanjay Das, Ran Elgedawy, Ethan Seefried, Ryan Burchfield, Tirthankar Ghosal",
  "category": "research",
  "topics": "agents-autonomy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-02T15:54:51.000Z",
  "fetched_at": "2026-07-14T16:30:05.529Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3203",
  "original_url": "https://arxiv.org/abs/2606.03812v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}