Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis
Sanjay Das, Ran Elgedawy, Ethan Seefried, Ryan Burchfield, Tirthankar Ghosal
Why It Matters
What makes this one worth your time
Improving hazard identification in safety-critical systems can significantly enhance operational safety, making this research relevant for AI engineers working in high-stakes domains.
HAZDIAL leverages multi-agent dialogue to enhance NLP-based hazard identification.
Summary
The paper introduces HAZDIAL, a framework for improving NLP-based hazard identification through structured multi-agent dialogue, comparing adversarial debate and constructive discussion modalities, and evaluating them against a curated dataset using standard and novel metrics.
Key contributions
- Introduction of the HAZDIAL framework for hazard identification.
- Systematic comparison of dialogue modalities in multi-agent systems.
- Evaluation using both standard classification and novel dialogue metrics.
Notable insights
- The use of structured agentic dialogue as opposed to single-turn inference for hazard identification.
- Comparison of adversarial and constructive dialogue modalities in multi-agent systems.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.03812v1 Announce Type: new Abstract: 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 dialogue-multi-agent, multi-turn interactions improves the quality of NLP- based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing an empirical evidence for dialogue-driven hazard analysis.