Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis
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
Record details
Published: 2 June 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Finance, VC & PE
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
arXiv · 30 May 2026
Does Persona Make LLMs K-pop Fans? A Pilot Study of LLM-Based Online Concert Audience Agents
arXiv · 5 June 2026
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
arXiv · 30 May 2026
Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents
arXiv · 27 May 2026
A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation
arXiv · 9 June 2026
Voluntary Collusion with Secret Tools in Competing LLM Agents
arXiv · 26 May 2026
How to cite this record
ethics.ai (2 June 2026), “Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis,” evidence record 3203, https://ethics.ai/record/3203 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.